We previously developed PaaSc, a method for inferring pathway activity from single-cell and spatial transcriptomics data. PaaSc employs multiple correspondence analysis (MCA) to simultaneously project cells and genes into a common latent space, then selects pathway-associated dimensions through linear regression to infer pathway activity scores. We validated PaaSc across diverse benchmarking datasets, including those with joint protein and RNA profiling, as well as large-scale cancer scRNA-seq cohorts. Compared with state-of-the-art methods, PaaSc demonstrated superior performance across multiple applications: scoring cell type-specific gene sets, identifying cell senescence-associated pathways, and exploring GWAS trait-associated cell types. Importantly, PaaSc maintained accuracy despite batch effects and showed robust performance across different data modalities, including scATAC-seq and spatial transcriptomics data. Despite these strengths, PaaSc had several limitations: it was implemented only in R and restricted to MCA-based dimensionality reduction. To address these constraints and expand functionality, we developed FeaSc, a Python package for inferring pathway activity from single-cell and spatial transcriptomic data. Feasc offers three key improvements: (i) it supports multiple dimensionality reduction methods beyond MCA, including PCA and NMF; (ii) it can infer signaling pathway activity (such as cytokine signaling) using ridge regression when pathways are defined as gene expression changes rather than gene sets; and (iii) for datasets with batch effects, Feasc integrates batch-corrected data from scVI for more accurate pathway activity inference.